摘要
目的 为实现低碳快捷处理水产养殖废水,探究利用小球藻处理废水的最优方案。方法 以某鲅鱼养殖基地水体为对象,利用AQUATOX软件建立水生态环境模型,根据不同的水产养殖废水浓度、水力停留时间和流量建立5个试验组,稀释后投加5 mg/L的小球藻藻种,模拟分析小球藻的处理效果,并将结果与实测数据进行对比。结果 投加小球藻的最适比例为:ρ(小球藻)∶ρ(TN)∶ρ(NH_(3)-N)∶ρ(TP)∶ρ(CODcr)=1∶16.8∶3.2∶0.8∶40;最适投加比例的小球藻对废水中的TN、TP、NH_(3)-N、CODCr的处理效率分别达到了73%、72.5%、94%、75%;藻生物量也达到了初始值的26倍。结论 应用AQUATOX模型可以快速实现小球藻处理水产养殖废水方案优化,TN、TP、NH_(3)-N和CODcr质量浓度模拟结果的平均相对误差分别为0.32%、2.52%、4.83%和0.10%。
In order to low carbon and fast treatment of aquaculture wastewater,the optimal solution for wastewater treatment by Chlorella was explored.The water body of a Spanish mackerel aquaculture base taken as the object,the water ecological environment model was established by using AQUATOX software.Five simulation groups were established according to different aquaculture wastewater concentrations,hydraulic retention times and flow rates.5 mg/L of Chlorella algae species after dilution were added in the simulation groups.The treatment performance of Chlorella was obtained by the simulation experiments.Based on the the results of simulation and the actual measured data,the optimal ratio of Chlorella spiked wasρ(Chlorella)∶ρ(TN)∶ρ(NH_(3)-N)∶ρ(TP)∶ρ(COD Cr)=1∶16.8∶3.2∶0.8∶40;the treatment efficiencies of Chlorella spiked with the optimal ratio on the TN,NH_(3)-N,TP,and CODCr in the wastewater reached 73%,94%,72.5%,and 75%,respectively.Biomass has also reached 26 times of the initial value.The application of the AQUATOX model can be quickly achieved to optimise the Chlorella treatment scheme for aquaculture wastewater,and the average relative errors of the simulation results for TN,TP,NH_(3)-N and CODcr mass concentrations were 0.32%,2.52%,4.83%and 0.10%,respectively.
作者
马兴冠
周欣彤
简文浩
MA Xingguan;ZHOU Xintong;JIAN Wenhao(School of Municipal and Environmental Engineering,Shenyang Jianzhu University,Shenyang,China,110168;Liaoning Province Liaohe River Basin Water Pollution Prevention and Control Research Institute,Shenyang,China,110168)
出处
《沈阳建筑大学学报（自然科学版）》
CAS
北大核心
2024年第2期378-384,共7页
Journal of Shenyang Jianzhu University：Natural Science
基金
国家水体污染控制与治理科技重大专项(2018ZX07601001)。
